<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Wireless indoor localization has generated recent research interest due to its numerous applications. This work investigates Wi-Fi based indoor localization using two variants of the fingerprinting approach. Specifically, we study the application of an artificial neural network (ANN) for implementing the fingerprinting approach and compare its localization performance with a probabilistic fingerprinting method that is based on maximum likelihood estimation (MLE) of the user location. We incorporate spatial correlation of fading into our investigations, which is often neglected in simulation studies and leads to erroneous location estimates. The localization performance is quantified in terms of accuracy, precision, robustness, and complexity. Multiple methods for handling the case of missing APs in online stage are investigated. Our results indicate that ANN-based fingerprinting outperforms the probabilistic approach for all performance metrics considered in this work.</p></abstract>

Wireless indoor localization has generated recent research interest due to its numerous applications. This work investigates Wi-Fi based indoor localization using two variants of the fingerprinting approach. Specifically, we study the application of an artificial neural network (ANN) for implementing the fingerprinting approach and compare its localization performance with a probabilistic fingerprinting method that is based on maximum likelihood estimation (MLE) of the user location. We incorporate spatial correlation of fading into our investigations, which is often neglected in simulation studies and leads to erroneous location estimates. The localization performance is quantified in terms of accuracy, precision, robustness, and complexity. Multiple methods for handling the case of missing APs in online stage are investigated. Our results indicate that ANN-based fingerprinting outperforms the probabilistic approach for all performance metrics considered in this work.

Wlan–Based Indoor Localization Using Neural Networks

Department of Electrical Engineering, COMSATS Institute of Information Technology, Islamabad,

Department of Physics, COMSATS Institute of Information Technology, Islamabad,

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"Wlan\u2013Based Indoor Localization Using Neural Networks"}

Wireless indoor localization has generated recent research interest due to its numerous applications. This work investigates Wi-Fi based indoor localization...

Wlan–Based Indoor Localization Using Neural Networks

Online veröffentlicht: 18. Sept. 2016

Seitenbereich: 299 - 306

Eingereicht: 23. Nov. 2015

DOI: https://doi.org/10.1515/jee-2016-0044

Schlüsselwörter
wireless local area network, localization, channel fading, neural network

© 2016 Faculty of Electrical Engineering and Information Technology, Slovak University of Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Wlan–Based Indoor Localization Using Neural Networks

Online veröffentlicht: 18. Sept. 2016

Seitenbereich: 299 - 306

Eingereicht: 23. Nov. 2015

DOI: https://doi.org/10.1515/jee-2016-0044

Schlüsselwörterwireless local area network, localization, channel fading, neural network

© 2016 Faculty of Electrical Engineering and Information Technology, Slovak University of Technology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Schlüsselwörter
wireless local area network, localization, channel fading, neural network